Design and Implementation of Risk Control Model Based on Deep Ensemble Learning Algorithm

The chapter delves into the critical issue of credit risk in internet credit loans and proposes a groundbreaking credit risk control model based on deep ensemble learning. By building a two-layer ensemble learner, the model effectively identifies potential defaulting users, achieving an impressive F1-Score of 0.98 on the Lending Club credit dataset. This innovative approach outperforms conventional methods like logistic regression and decision trees, showcasing excellent generalization capabilities. The model's architecture, including the selection of base learners and ensemble methods, is meticulously designed to capture both general and nuanced patterns in the data. The chapter also provides a comprehensive analysis of related work, experimental results, and future research directions, making it a valuable resource for professionals seeking to enhance credit risk management strategies.